Bibliographic record
Abstract
This paper proposes a puzzle about an agent’s beliefs that arises in certain cases where a person suffering from depression makes sincere assertions of negative self-evaluations because of her depression whilst also judging that her overall evidence supports the negation of her negative self-evaluations. I argue that the assertions of negative self-evaluations and the agent’s judgments about overall evidence each exhibit attributes that are characteristic of belief. I argue that the puzzle arises in cases where the agent in question is aware of each of her beliefs and is rational enough to prefer, not only avoiding conflicting beliefs, but also to prefer that her beliefs be based on relevant overall evidence, as opposed to depressive thought patterns. I consider a variety of theories that may map on to my case, and I discuss challenges or objections to each theory as it applies to the case. I think there are reasons to favor a theory that rejects the claim that the agent straightforwardly believes that her negative self-evaluations are true, so, after ruling out some unsatisfying accounts, I spend the first major portion of the paper discussing views that take this approach. The latter portion of the paper is spent discussing theories that put pressure on other premises or assumptions of the puzzle. My intention is to discuss the relationship between depression, belief, judgment, and rationality in a somewhat narrow context. I do not intend to speak to the relationship between belief, judgment, and rationality as it applies to depression in general. I leave it up to further inquiry to discuss depression more generally as it relates to the issues raised in this paper, and I leave it up to further inquiry to determine whether my proposed puzzle or its potential solutions have novel or interesting therapeutic implications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".